Method for estimating leaf water use efficiency by coupling deep learning with physical mechanism

By combining deep learning with physical mechanisms, a predictive model for carbon dioxide concentration inside leaves was constructed, which solved the problems of accuracy and continuity in estimating leaf water use efficiency and enabled the optimization of water consumption for precision irrigation of farmland and municipal greening.

CN117217074BActive Publication Date: 2026-07-03NANKAI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANKAI UNIV
Filing Date
2023-08-21
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately and continuously measure and estimate carbon dioxide concentrations within leaves, leading to significant uncertainty in estimates of leaf water use efficiency. Furthermore, deep learning models fail to reveal the physical mechanisms involved, thus hindering effective guidance for ecosystem management.

Method used

A method combining deep learning and physical mechanisms was adopted to predict the carbon dioxide concentration inside leaves by constructing a deep neural network model. The model was optimized to improve the estimation accuracy by combining multiple factors such as meteorology, soil, and vegetation, and utilizing multi-source remote sensing data and observation station data.

Benefits of technology

It enables continuous and high-precision estimation of leaf water use efficiency, reveals the physical mechanisms of influencing factors, and guides the optimization of water consumption for precision irrigation of farmland and municipal greening.

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Abstract

The application discloses a kind of coupling deep learning and physical mechanism's leaf water use efficiency estimation method, first, the environmental data and leaf water use efficiency of target area historical monitoring period are acquired;Then, construct the carbon dioxide concentration prediction model inside leaf, the trained carbon dioxide concentration prediction model inside leaf is used to predict carbon dioxide concentration inside leaf, the predicted value of carbon dioxide concentration inside leaf is substituted into formula to calculate leaf water use efficiency, the error between leaf water use efficiency estimation value and measured value is calculated, and the model is optimized by back propagation;Finally, the environmental data of target area monitoring period are acquired, and are input into the trained carbon dioxide concentration prediction model inside leaf, to obtain carbon dioxide concentration inside leaf;Leaf water use efficiency is calculated by carbon dioxide concentration formula inside leaf, and the estimation of leaf water use efficiency is completed.The method improves the estimation accuracy of leaf water use efficiency, and realizes continuous estimation.
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